Solid Waste Detection Using Enhanced YOLOv8 Lightweight Convolutional Neural Networks

As urbanization accelerates, solid waste management has become one of the key issues in urban governance. Accurate and efficient waste sorting is a crucial step in enhancing waste processing efficiency, promoting resource recycling, and achieving sustainable development. However, there are still many challenges inherent in today’s garbage detection methods. These challenges include the high computational cost of detection, the complexity of the detection background, and the difficulty in accurately evaluating the spatial relationship between rectangular detection frames during the inspection process. Therefore, this study improves the latest YOLOv8s object detection model, introducing a garbage detection model that balances light weight and detection performance. Firstly, this study introduces a newly designed structure, the CG-HGNetV2 network, to optimize the backbone network of YOLOv8s. This novel framework leverages local features, surrounding context, and global context to enhance the accuracy of semantic segmentation. It efficiently extracts features through a hierarchical approach, significantly reducing the computational cost of the model. Additionally, this study introduces an innovative network called MSE-AKConv, which integrates an attention module into the network architecture. The irregular convolution operations facilitate efficient feature extraction, enhancing the ability to extract valid information from complex backgrounds. In addition, this study introduces a new method to replace CIoU (complete intersection over union). On the basis of calculating IoU (intersection over union), it also considers the outer boundary of the two rectangles. By calculating the minimum distance between the boundaries, this method handles cases where boundaries are close but not overlapping, offering a more detailed similarity assessment than that provided by traditional IoU. In this study, the model was trained and evaluated using a publicly available dataset. Specifically, the model has improved the precision (P), recall rate (R), and mAP@50 (mean average precision at 50) by 4.80%, 0.10%, and 1.30%, while reducing model parameters by 6.55% and computational demand by 0.03%. This study not only provides an efficient automated solution for waste detection, but also opens up new avenues for ecological environmental protection.